000			03321cam a2200277zu 4500
001			88876285
003			FRCYB88876285
005			20250107232608.0
006			m o d
007			cr un
008			250108s2019 fr | o|||||0|0|||eng d
020			_a9781838551964
035			_aFRCYB88876285
040			_aFR-PaCSA _ben _c _erda
100	1		_aLiu, Yuxi (Hayden)
245	0	1	_aPyTorch 1.x Reinforcement Learning Cookbook _c['Liu, Yuxi (Hayden)']
264		1	_bPackt Publishing _c2019
300			_a p.
336			_btxt _2rdacontent
337			_bc _2rdamdedia
338			_bc _2rdacarrier
650		0	_a
700	0		_aLiu, Yuxi (Hayden)
856	4	0	_2Cyberlibris _uhttps://international.scholarvox.com/netsen/book/88876285 _qtext/html _a
520			_aImplement reinforcement learning techniques and algorithms with the help of real-world examples and recipes Key Features Use PyTorch 1.x to design and build self-learning artificial intelligence (AI) models Implement RL algorithms to solve control and optimization challenges faced by data scientists today Apply modern RL libraries to simulate a controlled environment for your projects Book Description Reinforcement learning (RL) is a branch of machine learning that has gained popularity in recent times. It allows you to train AI models that learn from their own actions and optimize their behavior. PyTorch has also emerged as the preferred tool for training RL models because of its efficiency and ease of use. With this book, you'll explore the important RL concepts and the implementation of algorithms in PyTorch 1.x. The recipes in the book, along with real-world examples, will help you master various RL techniques, such as dynamic programming, Monte Carlo simulations, temporal difference, and Q-learning. You'll also gain insights into industry-specific applications of these techniques. Later chapters will guide you through solving problems such as the multi-armed bandit problem and the cartpole problem using the multi-armed bandit algorithm and function approximation. You'll also learn how to use Deep Q-Networks to complete Atari games, along with how to effectively implement policy gradients. Finally, you'll discover how RL techniques are applied to Blackjack, Gridworld environments, internet advertising, and the Flappy Bird game. By the end of this book, you'll have developed the skills you need to implement popular RL algorithms and use RL techniques to solve real-world problems. What you will learn Use Q-learning and the state–action–reward–state–action (SARSA) algorithm to solve various Gridworld problems Develop a multi-armed bandit algorithm to optimize display advertising Scale up learning and control processes using Deep Q-Networks Simulate Markov Decision Processes, OpenAI Gym environments, and other common control problems Select and build RL models, evaluate their performance, and optimize and deploy them Use policy gradient methods to solve continuous RL problems Who this book is for Machine learning engineers, data scientists and AI researchers looking for quick solutions to different reinforcement learning problems will find this book useful. Although prior knowledge of machine learning concepts is required, experience with PyTorch will be useful but not necessary.
999			_c72493 _d72493